Constraining extreme precipitation projections using past precipitation variability

Projected changes of future precipitation extremes exhibit substantial uncertainties among climate models, posing grand challenges to climate actions and adaptation planning. Practical methods for narrowing the projection uncertainty remain elusive. Here, using large model ensembles, we show that the uncertainty in projections of future extratropical extreme precipitation is significantly correlated with the model representations of present-day precipitation variability. Models with weaker present-day precipitation variability tend to project larger increases in extreme precipitation occurrences under a given global warming increment. This relationship can be explained statistically using idealized distributions for precipitation. This emergent relationship provides a powerful constraint on future projections of extreme precipitation from observed present-day precipitation variability, which reduces projection uncertainty by 20–40% over extratropical regions. Because of the widespread impacts of extreme precipitation, this has not only provided useful insights into understanding uncertainties in current model projections, but is also expected to bring potential socio-economic benefits in climate change adaptation planning.


Supplementary Discussion. Testing the constraint for extreme precipitation intensity change
The current constraint is framed in terms of probability of extreme precipitation, here we test if it also works for extreme precipitation intensity change. The inter-model correlation between the present-day precipitation variability and extreme precipitation intensity change (in mm/day) is shown in Supplementary Fig. 6. The results are overall insensitive to different timescales of precipitation events and different measures of precipitation variability, thus only 5-day precipitation events (pr5d) are shown for brevity. No significant and systematic correlation exists in the mid-to-high latitudes, although there are patchy areas of statistically significant positive (negative) correlations in the tropical wet (subtropical dry) regions, which weaken for heavier extremes ( Supplementary Fig. 6).
Therefore, present-day precipitation variability plays a lesser role in the inter-model differences of extreme precipitation intensity change, and is thus not a powerful constraint. As extreme precipitation intensity is directly governed by thermodynamic (related to atmospheric humidity changes) and dynamic (related to circulation changes) processes, it may be more promising to connect projected changes in extreme precipitation intensity (and further, their model uncertainty) with key thermodynamics and dynamics. This is expected to improve the understanding of the projection uncertainty of extreme precipitation intensity and deserves dedicated research. The baseline is defined as the first 20 years in the 1pctCO2 experiments, and the warming conditions are defined at a 3°C global warming level (using 20-year periods) under the 1pctCO2 forcing relative to the baseline for each model. Here extreme precipitation is defined as those exceeding the 95th percentile in the baseline (R95); probability ratio of extreme precipitation is measured by the ratio of occurrence probability in the future period and the baseline; precipitation variability is measured by the difference between the 95th and 50th percentile precipitation events (R95-R50).
Statistically significant correlations at the 0.05 level are stippled.  ). e-f, Same as c-d, but for projected absolute changes in mean precipitation (in mm/day). Results show that multi-models agree better in the 10 projected mean precipitation changes (in absolute terms) in the extratropics with smaller inter-model standard deviation than in the tropics ( Supplementary Fig. 5f). This is contributed by both the higher model consistency in the simulated baseline mean precipitation and the fractional change in mean precipitation in the extratropics ( Supplementary Fig. 5b,d), which agree with the assumptions in the statistical framework. These results are consistent across seasons; only the June-to-August season is shown here. Supplementary Fig. 6 Inter-model correlation between the present-day precipitation variability and extreme precipitation intensity change. Inter-model correlation between the present-day precipitation variability and extreme precipitation intensity change (in mm/day) under a 3°C global warming increment in the joint ensemble of CMIP5 and CMIP6 using RCP8.5 and SSP5-8.5 scenario projections, respectively. The results for 5-day precipitation events (pr5d) are shown. Different extremes are considered, including the 95th percentile (R95; a-d), the 99th percentile (R99; e-h) and seasonal maximum precipitation event (i-l). Different seasons are shown, for December-to-February (DJF; the 1st row), March-to-May (MAM; the 2nd row), June-to-August (JJA; the 3rd row), and September-to-November (SON; the 4th row).
Here precipitation variability is measured by the difference between the 95th and 50th percentile precipitation events (R95-R50). Statistically significant correlations at the 0.05 level are stippled.
12 Supplementary Fig. 7 The emergent relationship in model simulations. Same as Fig. 1 in the main paper but for 1-day precipitation events (pr1d). Here extreme precipitation is defined as those exceeding the 95th percentile in the baseline (R95); precipitation variability is measured by the difference between the 95th and 50th percentile precipitation events (R95-R50).